The procurement and inversion of electromagnetic data has in recent years become a valuable tool in assessing the potential specific geophysical formations. Controlled-source electromagnetic (CSEM) data is often combined with other measurement data, such as seismic, gravity gradiometry, magnetotelluric (MT) or perhaps nearby well-logs to mention a few. In most mCSEM surveying applications, a mCSEM system comprises an electromagnetic sender, or antenna, that is either towed from a vessel, stationary in the body of water or on the seabed, and likewise a plurality of electromagnetic receivers that are either placed at known locations on the seabed or towed from a vessel or stationary in the body of water. The receivers can detect variations in electrical resistance as a function of variations in source signal, offset between the source and receiver and the properties of the geological layers, including their inherent electrical conductive properties. For instance, a hydrocarbon layer will exhibit a higher electrical resistance, ca. 20-300 ohm-m, than either seawater, ca. 0.3 ohm-m, or an overburden of sediment or rock, ca. 0.3-4 ohm-m. The acronyms CSEM or mCSEM are generally used interchangeably by those skilled in the art, and are not meant to be delimiting in any technical sense, unless explicitly specified. The terms resistivity or resistance are also used interchangeably by those skilled in the art, and are not meant to be delimiting in any technical sense, unless explicitly specified. The various types of measurement methods, due to their inherent designs, often acquire data with different temporal and spatial scales. As these data sets have increased in size and complexity, the challenges in processing such large data sets has also increased. Inversion processing techniques have been developed in step with instrumentation, whereby the aim of the inversion is to optimize the parameters of a model to find the best fit between the calculated value and the measured data whereby the measured data can be used to constrain models.
Prior art modeling methods are based on applying resistance directly from mCSEM inversion results, and inserting these into an appropriate saturation-resistivity relation, such as Archie's equation or similar. Data inversion can be described as providing an estimate of geophysical properties by way of updating an initial model based upon available the measured data and other prior knowledge from a given area. In brief, Archie's equation is an empirical quantitative relationship between porosity, electrical conductivity, and brine saturation of rocks. The equation is a basis for modern well log interpretation as it relates borehole electrical conductivity measurements to hydrocarbon saturations. There are various forms of Archie's equation, such as the following general form:Sw=[(a/Φm)*(Rw/Rt)](1/n) Where:    Sw: water saturation    Φ: porosity    Rw: formation water resistivity    Rt: observed bulk resistivity    a: a constant (usually about 1)    m: cementation factor (usually about 2)    n: saturation exponent (usually about 2)
Assuming porosity and water and bulk resistivity (and exponents in Archie's equation) are known, the hydrocarbon saturation (SHC) estimate can be obtained from the simple algebraic expression: SHC=1−Sw. This workflow assumes in principle that resistivity, porosity and saturation are constant within the CSEM discretization.
Published documentation describing the existing technology is referenced at the end of the present section.
At present there are several challenges associated with the current state of the art mCSEM data evaluation methods:
1). The resistances from mCSEM inversions can be inaccurate due to reasons such as weak optimization algorithms due to computational constraints, the use of lower dimensional (not proper 3D) inversions, and the low frequency of the mCSEM signal can yield observations that includes a convolution of both the signal above and below the hydrocarbon reservoir.2). In addition, all parameters in the water saturation formula (for instance, Archie's equation) and mCSEM resistivities are associated with uncertainties. True resistivity is very difficult to determine. This is an indication that the procedure should be stochastic for incorporation into a final estimation.3). Further, mCSEM resistances are coarse scale measurements. Variations within the reservoir column will affect the measurement, and the assumption of constant porosity and saturation in the reservoir is very often not valid.